Detection Techniques in Uplink Massive MIMO Systems

Datta, Arijit; Mandloi, Manish; Bhatia, Vimal

doi:10.1007/978-981-15-6390-4_3

Cited by 2 publications

(1 citation statement)

References 37 publications

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“…In the case of an uplink MIMO channel, when the number of (single-antenna) users M is far less than the number of BS antennas N , linear processing techniques, such as zero-forcing (ZF) or minimum mean-square error (MMSE), attain near-optimal performance [ 9 ], and detectors based on Neumann series expansions can further reduce that complexity [ 10 ]. However, when

, i.e., as the ratio of the number of antenna elements on both sides of a MIMO system gets closer to 1, linear detection is well-known to severely degrade [ 11 , 12 ]. It is therefore important to use detection methods that perform close to maximum likelihood (ML), i.e., exhaustive search, while yielding low complexity.…”

Section: Introductionmentioning

confidence: 99%

Gibbs Sampling Detection for Large MIMO and MTC Uplinks with Adaptive Modulation

Rosário

Monteiro

2022

Sensors

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Wireless networks beyond 5G will mostly be serving myriads of sensors and other machine-type communications (MTC), with each device having different requirements in respect to latency, error rate, energy consumption, spectral efficiency or other specifications. Multiple-input multiple-output (MIMO) systems remain a central technology towards 6G, and in cases where massive antenna arrays or cell-free networks are not possible to deploy and only moderately large antenna arrays are allowed, the detection problem at the base-station cannot rely on zero-forcing or matched filters and more complex detection schemes have to be used. The main challenge is to find low complexity, hardware feasible methods that are able to attain near optimal performance. Randomized algorithms based on Gibbs sampling (GS) were proven to perform very close to the optimal detection, even for moderately large antenna arrays, while yielding an acceptable number of operations. However, their performance is highly dependent on the chosen “temperature” parameter (TP). In this paper, we propose and study an optimized variant of the GS method, denoted by triple mixed GS, and where three distinct values for the TP are considered. The method exhibits faster convergence rates than the existing ones in the literature, hence requiring fewer iterations to achieve a target bit error rate. The proposed detector is suitable for symmetric large MIMO systems, however the proposed fixed complexity detector is highly suitable to spectrally efficient adaptively modulated MIMO (AM-MIMO) systems where different types of devices upload information at different bit rates or have different requirements regarding spectral efficiency. The proposed receiver is shown to attain quasi-optimal performance in both scenarios.

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Section: Introductionmentioning

confidence: 99%

Gibbs Sampling Detection for Large MIMO and MTC Uplinks with Adaptive Modulation

Rosário

Monteiro

2022

Sensors

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Performance Analysis of MIMO Detection Algorithms with BPSK and PAM Modulation

Padmasree¹,

Naik²

2022

IJRTE

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One of the best strategies for worthwhile Communication in wireless technologies is MIMO i.e., Multiple input and Multiple output systems where the information exchange is happened through multiple antennas. The Wireless transmission of data/signal suffers from fading and interference effects which creates a problem for signal recovery in wireless communication hence these effects can be controlled by the equalizer. The MIMO system uses multiple antennas at the transmitter and receiver to exploit multipath propagation. The paper emphasizes various channel equalization algorithms recognized for sub-optimum MIMO detection which include Zero Forcing (ZF), Minimum Mean Square Error (MMSE), ZF-SIC, and MMSE-SIC equalizers, the performance is evaluated for 2*2 and 4*4 MIMO wireless channels using various modulation schemes in which BER is taken for consideration. The implementation work is carried out through MATLAB toolbox version 2018b.

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Detection Techniques in Uplink Massive MIMO Systems

Cited by 2 publications

References 37 publications

Gibbs Sampling Detection for Large MIMO and MTC Uplinks with Adaptive Modulation

Gibbs Sampling Detection for Large MIMO and MTC Uplinks with Adaptive Modulation

Performance Analysis of MIMO Detection Algorithms with BPSK and PAM Modulation

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